



'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    List[Any]		,     __snake_case					:    List[str]		,     __snake_case					:    str      ):
      super().__init__()

      # make sure scheduler can always be converted to DDIM
      a		:   Any	  =	DDIMScheduler.from_config(scheduler.config      )

      self.register_modules(unet=__snake_case		,     scheduler=__snake_case      )







    @torch.no_grad()
    def __call__( self					:    Any		,     __snake_case					:    int = 1		,     __snake_case					:    Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     __snake_case					:    float = 0.0		,     __snake_case					:    int = 50		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    Optional[str] = "pil"		,     __snake_case					:    bool = True		,     ):

      # Sample gaussian noise to begin loop
      if isinstance(self.unet.config.sample_size		,     __snake_case      ):
        a		:   Dict	  =	(
            batch_size,
            self.unet.config.in_channels,
            self.unet.config.sample_size,
            self.unet.config.sample_size,
        )
      else:
        a		:   Optional[Any]	  =	(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

      if isinstance(__snake_case		,     __snake_case      ) and len(__snake_case      ) != batch_size:
        raise ValueError(
            F"""You have passed a list of generators of length {len(__snake_case      )}, but requested an effective batch"""
            F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""      )

      a		:   Dict	  =	randn_tensor(__snake_case		,     generator=__snake_case		,     device=self.device		,     dtype=self.unet.dtype      )

      # set step values
      self.scheduler.set_timesteps(__snake_case      )

      for t in self.progress_bar(self.scheduler.timesteps      ):
        # 1. predict noise model_output
        a		:   int	  =	self.unet(__snake_case		,     __snake_case      ).sample

        # 2. predict previous mean of image x_t-1 and add variance depending on eta
        # eta corresponds to η in paper and should be between [0, 1]
        # do x_t -> x_t-1
        a		:   Optional[int]	  =	self.scheduler.step(
            __snake_case		,     __snake_case		,     __snake_case		,     eta=__snake_case		,     use_clipped_model_output=__snake_case		,     generator=__snake_case      ).prev_sample

      a		:   Dict	  =	(image / 2 + 0.5).clamp(0		,     1      )
      a		:   Optional[Any]	  =	image.cpu().permute(0		,     2		,     3		,     1      ).numpy()
      if output_type == "pil":
        a		:   Optional[int]	  =	self.numpy_to_pil(__snake_case      )

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=__snake_case      )




'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:   Tuple					=		logging.get_logger(__name__)

lowerCAmelCase:   Any					=		{
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """pegasus"""
    lowercase__					=       ["""past_key_values"""]
    lowercase__					=       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







    def __init__( self					:    Union[str, Any]		,     __snake_case					:    Tuple=5_02_65		,     __snake_case					:    List[Any]=10_24		,     __snake_case					:    Optional[Any]=12		,     __snake_case					:    Tuple=40_96		,     __snake_case					:    str=16		,     __snake_case					:    int=12		,     __snake_case					:    Tuple=40_96		,     __snake_case					:    int=16		,     __snake_case					:    Tuple=0.0		,     __snake_case					:    Any=0.0		,     __snake_case					:    Any=True		,     __snake_case					:    Optional[int]=True		,     __snake_case					:    Any="gelu"		,     __snake_case					:    str=10_24		,     __snake_case					:    Dict=0.1		,     __snake_case					:    List[Any]=0.0		,     __snake_case					:    str=0.0		,     __snake_case					:    str=0.02		,     __snake_case					:    Union[str, Any]=0		,     __snake_case					:    List[Any]=False		,     __snake_case					:    Optional[Any]=0		,     __snake_case					:    str=1		,     __snake_case					:    Any=1		,     **__snake_case					:    int		,     ):
      a		:   Optional[int]	  =	vocab_size
      a		:   List[str]	  =	max_position_embeddings
      a		:   List[Any]	  =	d_model
      a		:   Tuple	  =	encoder_ffn_dim
      a		:   List[str]	  =	encoder_layers
      a		:   str	  =	encoder_attention_heads
      a		:   str	  =	decoder_ffn_dim
      a		:   Optional[int]	  =	decoder_layers
      a		:   Optional[int]	  =	decoder_attention_heads
      a		:   Optional[int]	  =	dropout
      a		:   Any	  =	attention_dropout
      a		:   str	  =	activation_dropout
      a		:   List[Any]	  =	activation_function
      a		:   Union[str, Any]	  =	init_std
      a		:   List[Any]	  =	encoder_layerdrop
      a		:   Union[str, Any]	  =	decoder_layerdrop
      a		:   List[Any]	  =	use_cache
      a		:   Any	  =	encoder_layers
      a		:   Optional[int]	  =	scale_embedding  # scale factor will be sqrt(d_model) if True
      super().__init__(
          pad_token_id=__snake_case		,     eos_token_id=__snake_case		,     is_encoder_decoder=__snake_case		,     decoder_start_token_id=__snake_case		,     forced_eos_token_id=__snake_case		,     **__snake_case		,     )







    @property
    def      lowercase_			( self					:    int      ):
      return self.encoder_attention_heads







    @property
    def      lowercase_			( self					:    int      ):
      return self.d_model